ABSTRACT
Smartphone users are increasingly shifting to using apps as "gateways" to Internet services rather than traditional web browsers. App marketplaces for iOS, Android, and Windows Phone platforms have made it attractive for developers to deploy apps and easy for users to discover and start using many network-enabled apps quickly. For example, it was recently reported that the iOS AppStore has more than 350K apps and more than 10 billion downloads. Furthermore, the appearance of tablets and mobile devices with other form factors, which also use these marketplaces, has increased the diversity in apps and their user population. Despite the increasing importance of apps as gateways to network services, we have a much sparser understanding of how, where, and when they are used compared to traditional web services, particularly at scale. This paper takes a first step in addressing this knowledge gap by presenting results on app usage at a national level using anonymized network measurements from a tier-1 cellular carrier in the U.S. We identify traffic from distinct marketplace apps based on HTTP signatures and present aggregate results on their spatial and temporal prevalence, locality, and correlation.
- Apple. Apple's App Store Downloads Top 10 Billion. http://www.apple.com/pr/library/2011/01/22appstore.html.Google Scholar
- A. Balasubramanian, R. Mahajan, and A. Venkataramani. Augmenting Mobile 3G Using WiFi: Measurement, System Design, and Implementation. In Proc. ACM MOBISYS, 2010. Google ScholarDigital Library
- X. Bao and R. Roy Choudhury. MoVi: mobile phone based video highlights via collaborative sensing. In Proc. ACM MOBISYS, 2010. Google ScholarDigital Library
- I. Constandache, X. Bao, M. Azizyan, and R. R. Choudhury. Did you see Bob?: human localization using mobile phones. In Proc. ACM MOBICOM, 2010. Google ScholarDigital Library
- E. Cuervo, A. Balasubramanian, D. ki Cho, A. Wolman, S. Saroiu, R. Ch, and P. Bahl. MAUI: Making smartphones last longer with code offload. In Proc. ACM MOBISYS, 2010. Google ScholarDigital Library
- W. Enck, P. Gilbert, B.-G. Chun, L. P. Cox, J. Jung, P. McDaniel, and A. N. Sheth. TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones. In USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2010. Google ScholarDigital Library
- H. Falaki, D. Lymberopoulos, R. Mahajan, R. Govindan, S. Kandula, and D. Estrin. Diversity in Smartphone Usage. In Proc. ACM MOBISYS, 2010. Google ScholarDigital Library
- H. Falaki, D. Lymberopoulos, R. Mahajan, S. Kandula, and D. Estrin. A first look at traffic on smartphones. In Proc. ACM SIGCOMM IMC, 2010. Google ScholarDigital Library
- Federal Emergency Management Agency. Tornado Activity in the United States. http://www.fema.gov/plan/prevent/saferoom/tsfs02_torn_activity.shtm.Google Scholar
- M. Ficek, T. Pop, P. Vláčil, K. Dufková, L. Kencl, and M. Tomek. Performance study of active tracking in a cellular network using a modular signaling platform. In Proc. ACM MOBISYS, 2010. Google ScholarDigital Library
- A. Gember, A. Anand, and A. Akella. A Comparative Study of Handheld and Non-Handheld Traffic in Campus WiFi Networks. In Proc. International Conference on Passive and Active Network Measurement (PAM), 2011. Google ScholarDigital Library
- Google. Eric Schmidt at Mobile World Congress 2011. http://www.youtube.com/watch?v=ClkQA2Lb_iE&feature=related.Google Scholar
- B. D. Higgins, A. Reda, T. Alperovich, J. Flinn, T. J. Giuli, B. Noble, and D. Watson. Intentional networking: opportunistic exploitation of mobile network diversity. In Proc. ACM MOBICOM, 2010. Google ScholarDigital Library
- J. Huang, Q. Xu, B. Tiwana, Z. M. Mao, M. Zhang, and P. Bahl. Anatomizing Application Performance Differences on Smartphones. In Proc. ACM MOBISYS, 2010. Google ScholarDigital Library
- R. Keralapura, A. Nucci, Z.-L. Zhang, and L. Gao. Profiling users in a 3g network using hourglass co-clustering. In Proc. ACM MOBICOM, 2010. Google ScholarDigital Library
- K. A. Li, T. Y. Sohn, S. Huang, and W. G. Griswold. Peopletones: a system for the detection and notification of buddy proximity on mobile phones. In Proc. ACM MOBISYS, 2008. Google ScholarDigital Library
- Y. Liu, A. Rahmati, Y. Huang, H. Jang, L. Zhong, Y. Zhang, and S. Zhang. xShare: supporting impromptu sharing of mobile phones. In Proc. ACM MOBISYS, 2009. Google ScholarDigital Library
- G. Maier, F. Schneider, and A. Feldmann. A first look at mobile hand-held device traffic. In Proc. International Conference on Passive and Active Network Measurement (PAM), 2010. Google ScholarDigital Library
- National Hurricane Center. National Hurricane Center. http://www.nhc.noaa.gov/pdf/TAFB_Trifold.pdf.Google Scholar
- F. Qian, Z. Wang, A. Gerber, Z. M. Mao, S. Sen, and O. Spatscheck. Characterizing radio resource allocation for 3G networks. In Proc. ACM SIGCOMM IMC, 2010. Google ScholarDigital Library
- F. Qian, Z. Wang, A. Gerber, Z. M. Mao, S. Sen, and O. Spatscheck. Profiling Resource Usage for Mobile Applications: a Cross-layer Approach. In Proc. ACM MOBISYS, 2010. Google ScholarDigital Library
- I. Trestian, S. Ranjan, A. Kuzmanovic, and A. Nucci. Measuring serendipity: connecting people, locations and interests in a mobile 3G network. In Proc. ACM SIGCOMM IMC, 2009. Google ScholarDigital Library
- I. Trestian, S. Ranjan, A. Kuzmanovic, and A. Nucci. Taming User-Generated Content in Mobile. Networks via Drop Zones. In Proc. IEEE INFOCOM, 2009.Google Scholar
- Wikipedia. High-Speed Downlink Packet Access. http://en.wikipedia.org/wiki/High-Speed_Downlink_Packet_Access.Google Scholar
- Wikipedia. High-Speed Uplink Packet Access. http://en.wikipedia.org/wiki/High-Speed_Uplink_Packet_Access.Google Scholar
- W. Woerndl, C. Schueller, and R. Wojtech. A Hybrid Recommender System for Context-aware Recommendations of Mobile Applications. In Proc. IEEE ICDE, 2007. Google ScholarDigital Library
- Q. Xu, A. Gerber, Z. M. Mao, and J. Pang. Acculoc: Practical localization of peformance measurement in 3g networks. In Proc. ACM MOBISYS, 2011. Google ScholarDigital Library
- Q. Xu, J. Huang, Z. Wang, F. Qian, A. Gerber, and Z. M. Mao. Cellular data network infrastructure characterization and implication on mobile content placement. In Proc. ACM SIGMETRICS, 2011. Google ScholarDigital Library
- B. Yan and G. Chen. AppJoy: Personalized Mobile Application Discovery. In Proc. ACM MOBISYS, 2011. Google ScholarDigital Library
- L. Zhang, B. Tiwana, Z. Qian, Z. Wang, R. P. Dick, Z. M. Mao, and L. Yang. Accurate online power estimation and automatic battery behavior based power model generation for smartphones. In Proc. IEEE/ACM/IFIP CODES+ISSS, 2010. Google ScholarDigital Library
Index Terms
- Identifying diverse usage behaviors of smartphone apps
Recommendations
Discovering different kinds of smartphone users through their application usage behaviors
UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous ComputingUnderstanding smartphone users is fundamental for creating better smartphones, and improving the smartphone usage experience and generating generalizable and reproducible research. However, smartphone manufacturers and most of the mobile computing ...
Characterizing Smartphone Usage Patterns from Millions of Android Users
IMC '15: Proceedings of the 2015 Internet Measurement Conferencehe prevalence of smart devices has promoted the popular- ity of mobile applications (a.k.a. apps) in recent years. A number of interesting and important questions remain unan- swered, such as why a user likes/dislikes an app, how an app becomes popular ...
Investigating Login Features in Smartphone Apps
UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable ComputersRecent revelations about data breaches have heightened users' consciousness about the privacy of their online activity. An often overlooked avenue of collection of users' personal information are registration processes and/or social logins, such as ...
Comments